This is a collection of bibliographies of scientific literature in computer science from various sources, covering most aspects of computer science. The bibliographies are updated weekly from their original locations such that you'll always find the most recent versions here. · http://liinwww.ira.uka.de/bibliography/index.html

If you are starting with Neural Networks you should check out my online book on the subject. It contains over 300 pages of information on Neural Network Programming in Java. You can access it here. · http://www.jeffheaton.com/ai

One thing I really love with the Python programming language is its incredible extensibility. Here’s a list of 50 awesome modules for Python, covering almost all needs: Databases, GUIs, Images, Sound, OS interaction, Web, and more. · http://www.catswhocode.com/blog/featured/python-50-modules-for-all-needs-10

Here's a realtively simple way to implement data versioning in a database, in a way that should be scalable as well. It only needs a couple of support tables and a single function and can apply versioning across multiple data sets concurrently. · http://www.users.on.net/~notzed/software/data-versioning.html

"RRDtool is the Open Source industry standard, high performance data logging and graphing system for time series data. It stores the data in a very compact way that will not expand over time, and it can create beautiful graphs." · http://coreygoldberg.blogspot.com/2008/05/python-rrdpy-round-robin-databases.html

Getting started with Jersey is very easy. First, download the latest distribution of Jersey and unzip it. If you are using NetBeans IDE 6.x, you do not need to download the Jersey distribution. Instead, install the RESTful Web Services plugin from the Plu · https://jersey.dev.java.net/use/getting-started.html

This project intends to be a full implementation of the Atom Syndication Format (RFC 4287) and Atom Publishing Protocol for the Django web framework. · http://code.google.com/p/django-atompub/

In analyzing my data I wanted to classify it with a naive Bayesian classifier. I wasn't sure I had the math right, so I wrote a tiny abstract classifier to test with. The code is pretty cool: · http://funcall.blogspot.com/2007/08/naive-bayesian-classifier.html

Python is easy to learn, and more powerful than Bash. I wasn’t supposed to tell you this–it’s supposed to be a secret. · http://www.redhatmagazine.com/2008/02/07/python-for-bash-scripters-a-well-kept-secret/